Variation in the genome is a powerful instrument for learning about differences between individuals. It avoids the problems of reverse causality, is abundant in natural populations, and can be generated in a targeted manner in the lab.
I will talk about our approaches for using different types of genetic variation to understand cellular traits, and the computational models we developed for the purpose. We previously traced the signal from common alleles to RNA abundance and protein levels, as well as cellular growth rate, finding both shared and independent causes of variability. Currently, we employ genome engineering methods to learn about gene essentiality.
To undertake experiments at scale, we have developed models to predict the mutational outcome of CRISPR/Cas9 editing, created a tool to efficiently analyse genome-wide screens, and optimized the requisite guide RNA libraries. I will present the results of these studies, and preliminary findings of variation in gene essentiality between individuals.

About Leopold

Leo is a geneticist with broad interests in all areas of genomics. He focuses on figuring out most important features to measure in individual cells, creating accurate computational models for their readouts, and finding out how they are changed by genomes and environment.

After double majoring in Computer Science and Mathematics at MIT, Leo received his PhD from the University of Cambridge in 2011 under the supervision of Richard Durbin. In his thesis work, he studied sources of variation in gene expression, as well as using selection to map the genetic basis of fitness traits. This entailed creating probabilistic models of the generative processes, and performing inference in them to estimate the desired properties of the world. The work was awarded the Grand Prize of life sciences PhDs in his native Estonia.

After graduation, Leo wanted to train in experimental high throughput genetics, and joined the labs of Brenda Andrews and Charles Boone in the University of Toronto as a Canadian Institute for Advanced Research Global Scholar. In his postdoctoral work, he carried out genome-wide and targeted genetic screens in yeast using collections of fluorescent proteins, and transcriptional modulation using the CRISPR/Cas9 system. He continued his research in systems genetics as a Marie Curie Fellow, completing the second half of the postdoc in the lab of Lars Steinmetz in EMBL Heidelberg and Stanford University.

In the summer of 2015, Leo joined the Sanger Institute faculty as part of the Computational Genomics Programme. His group uses natural and engineered genetic variation, combined with approaches for transcriptional modulation, to identify perturbations that have large effects on cellular traits, such as cell viability, doubling time, or reporter activity.

We use cookies and other tracking technologies to improve your browsing experience on our site, provide social media features and to analyze site traffic. We also share information about your use of our site with our social media and analytics partners who may combine it with other information that you've provided to them or that they've collected from your use of their services. By choosing I Accept, you consent to our use of cookies and other tracking technologies in accordance with our Privacy Policy.